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International audience; Dendritic ecological networks (DENs) are a unique form of ecological networks that exhibit a dendritic network topology (e.g. stream and cave networks or plant architecture). DENs have a dual spatial representation; as points within the network and as points in geographical space. Consequently, some analytical methods used to quantify relationships in other types of ecological networks, or in 2-D space, may be inadequate for studying the influence of structure and connectivity on ecological processes within DENs. We propose a conceptual taxonomy of network analysis methods that account for DEN characteristics to varying degrees and provide a synthesis of the different approaches within the context of stream ecology. Within this context, we summarise the key innovations of a new family of spatial statistical models that describe spatial relationships in DENs. Finally, we discuss how different network analyses may be combined to address more complex and novel research questions. While our main focus is streams, the taxonomy of network analyses is also relevant anywhere spatial patterns in both network and 2-D space can be used to explore the influence of multi-scale processes on biota and their habitat (e.g. plant morphology and pest infestation, or preferential migration along stream or road corridors).

Despite the importance of habitat in determining species distribution and persistence, habitat dynamics are rarely modeled in studies of metapopulations. We used an integrated habitat-occupancy model to simultaneously quantify habitat change, site fidelity, and local colonization and extinction rates for larvae of a suite of Great Plains stream fishes in the Arikaree River, eastern Colorado, USA, across three years. Sites were located along a gradient of flow intermittency and groundwater connectivity. Hydrology varied across years: the first and third being relatively wet and the second dry. Despite hydrologic variation, our results indicated that site suitability was random from one year to the next. Occupancy probabilities were also independent of previous habitat and occupancy state for most species, indicating little site fidelity. Climate and groundwater connectivity were important drivers of local extinction and colonization, but the importance of groundwater differed between periods. Across species, site extinction probabilities were highest during the transition from wet to dry conditions (range: 0.52-0.98), and the effect of groundwater was apparent with higher extinction probabilities for sites not fed by groundwater. Colonization probabilities during this period were relatively low for both previously dry sites (range: 0.02-0.38) and previously wet sites (range: 0.02-0.43). In contrast, no sites dried or remained dry during the transition from dry to wet conditions, yielding lower but still substantial extinction probabilities (range: 0.16-0.63) and higher colonization probabilities (range: 0.06-0.86), with little difference among sites with and without groundwater. This approach of jointly modeling both habitat change and species occupancy will likely be useful to incorporate effects of dynamic habitat on metapopulation processes and to better inform appropriate conservation actions.

Processes that influence habitat selection in landscapes involve the interaction of habitat composition and configuration and are particularly important for species with complex life cycles. We assessed the relative influence of landscape spatial processes and local habitat characteristics on patterns in the distribution and abundance of spawning steelhead (Oncorhynchus mykiss), a threatened salmonid fish, across ∼15,000 stream km in the John Day River basin, Oregon, USA. We used hurdle regression and a multi-model information theoretic approach to identify the relative importance of covariates representing key aspects of the steelhead life cycle (e.g., site access, spawning habitat quality, juvenile survival) at two spatial scales: within 2-km long survey reaches (local sites) and ecological neighborhoods (5 km) surrounding the local sites. Based on Akaike's Information Criterion, models that included covariates describing ecological neighborhoods provided the best description of the distribution and abundance of steelhead spawning given the data. Among these covariates, our representation of offspring survival (growing-season-degree-days, °C) had the strongest effect size (7x) relative to other predictors. Predictive performances of model-averaged composite and neighborhood-only models were better than a site-only model based on both occurrence (percentage of sites correctly classified = 0.80±0.03 SD, 0.78±0.02 vs. 0.62±0.05, respectively) and counts (root mean square error = 3.37, 3.93 vs. 5.57, respectively). The importance of both temperature and stream flow for steelhead spawning suggest this species may be highly sensitive to impacts of land and water uses, and to projected climate impacts in the region and that landscape context, complementation, and connectivity will drive how this species responds to future environments.